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Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods and materials › Cerebellar processing ↔ src/cersuit_main.m, the whole file · a weak match · score 0.69 · native space, SUIT atlas, isolation, pre, reslicing, isolated
  2. [2] § Methods and materials › Cerebellar processing ↔ src/transform_SUIT_to_native.m, the whole file · a weak match · score 0.57 · original native space, SUIT atlas, transformation, reslicing

Paper

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The authors' code

MATLAB · 120 lines · 3.8 KB · no license · 1 match

  1. function cersuit_main(inp)
  3. % Work in the output directory
  4. cd(inp.out_dir);
  6. % We need SPM running
  7. spm_jobman('initcfg');
  9. % Initial coreg of T1 to atlas via gray matter segmentation
  10. disp('Init coreg')
  11. coreg_txt = init_coreg_translation_only('gm.nii',inp.out_dir);
  12. apply_init_coreg(coreg_txt,'t1.nii',inp.out_dir);
  14. % Segment the cerebellum
  15. disp('Segment cerebellum')
  16. suit_isolate_seg({'rt1.nii'},'maskp',str2double(inp.maskp),'keeptempfiles',1);
  18. % Apply ACAPULCO mask if present (following
  19. % https://pubmed.ncbi.nlm.nih.gov/35188124/)
  20. isomask = 'c_rt1_pcereb.nii';
  21. if ~isempty(inp.use_acapulco)
  22. disp('Using ACAPULCO mask')
  23. apply_init_coreg(coreg_txt,'acapulco.nii',inp.out_dir);
  24. clear matlabbatch
  25. matlabbatch{1}.spm.util.imcalc.input = {
  26. 'racapulco.nii'
  27. 'c_rt1_seg1.nii'
  28. 'c_rt1_seg2.nii'
  29. };
  30. matlabbatch{1}.spm.util.imcalc.output = isomask;
  31. matlabbatch{1}.spm.util.imcalc.outdir = {inp.out_dir};
  32. matlabbatch{1}.spm.util.imcalc.expression = '(((i1>0).*i2)+i3)>0.1';
  33. matlabbatch{1}.spm.util.imcalc.var = struct('name', {}, 'value', {});
  34. matlabbatch{1}.spm.util.imcalc.options.dmtx = 0;
  35. matlabbatch{1}.spm.util.imcalc.options.mask = 0;
  36. matlabbatch{1}.spm.util.imcalc.options.interp = 1;
  37. matlabbatch{1}.spm.util.imcalc.options.dtype = 4;
  38. spm_jobman('run',matlabbatch);
  39. end
  41. % Estimate the atlas space warp
  42. disp('Estimate warp')
  43. job = struct();
  44. job.subjND(1).gray = {'c_rt1_seg1.nii'};
  45. job.subjND(1).white = {'c_rt1_seg2.nii'};
  46. job.subjND(1).isolation = {'c_rt1_pcereb.nii'};
  47. suit_normalize_dartel(job);
  49. % Create several images in SUIT atlas space, unmodulated, interpolated
  50. disp('Resample images')
  51. for m = {'c_rt1.nii','c_rt1_seg1.nii','c_rt1_seg2.nii'}
  52. job = struct();
  53. job.subj.affineTr = {'Affine_c_rt1_seg1.mat'};
  54. job.subj.flowfield = {'u_a_c_rt1_seg1.nii'};
  55. job.subj.mask = {'c_rt1_pcereb.nii'};
  56. job.subj.resample = m(1);
  57. job.interp = 1;
  58. job.jactransf = 0;
  59. suit_reslice_dartel(job);
  60. movefile(['wd' m{1}],['w' m{1}]);
  61. end
  63. % Create cer mask atlas space, unmodulated, not interpolated
  64. disp('Resample mask')
  65. job = struct();
  66. job.subj.affineTr = {'Affine_c_rt1_seg1.mat'};
  67. job.subj.flowfield = {'u_a_c_rt1_seg1.nii'};
  68. job.subj.mask = {'c_rt1_pcereb.nii'};
  69. job.subj.resample = {'c_rt1_pcereb.nii'};
  70. job.interp = 0;
  71. job.jactransf = 0;
  72. suit_reslice_dartel(job);
  73. movefile('wdc_rt1_pcereb.nii','wc_rt1_pcereb.nii');
  75. % Create modulated grey and white matter images in atlas space
  76. disp('Resample/modulate')
  77. for m = {'c_rt1_seg1.nii','c_rt1_seg2.nii'}
  78. job = struct();
  79. job.subj.affineTr = {'Affine_c_rt1_seg1.mat'};
  80. job.subj.flowfield = {'u_a_c_rt1_seg1.nii'};
  81. job.subj.resample = m(1);
  82. job.subj.mask = {'c_rt1_pcereb.nii'};
  83. job.interp = 1;
  84. job.jactransf = 1;
  85. suit_reslice_dartel(job);
  86. end
  88. % Resample the atlas to subject space
  89. disp('Resample atlases')
  90. job = struct();
  91. job.Affine = {'Affine_c_rt1_seg1.mat'};
  92. job.flowfield = {'u_a_c_rt1_seg1.nii'};
  93. job.subj.mask = {'c_rt1_pcereb.nii'};
  94. job.ref = {'c_rt1.nii'};
  95. job.interp = 0;
  96. for m = {'atl-Anatom','atl-Buckner7','atl-Buckner17','atl-Ji10','atl-MDTB10'}
  97. disp([' ' m{1}])
  98. job.resample = {which([m{1} '_sp-SUIT.nii'])};
  99. suit_reslice_dartel_inv(job);
  100. apply_reverse_coreg(coreg_txt,['iw_' m{1} '_sp-SUIT_u_a_c_rt1_seg1.nii']);
  101. end
  103. % Copy atlas space atlases
  104. disp('Copy atlases')
  105. for m = {'atl-Anatom','atl-Buckner7','atl-Buckner17','atl-Ji10','atl-MDTB10'}
  106. copyfile(which([m{1} '_sp-SUIT.nii']),inp.out_dir);
  107. copyfile(which([m{1} '.lut']),inp.out_dir);
  108. end
  110. % Get the native space output images back to pre-coreg native space
  111. % (original T1 space). Header is updated in place.
  112. disp('Resample to native')
  113. for m = {'c_rt1','c_rt1_pcereb','c_rt1_seg1','c_rt1_seg2'}
  114. apply_reverse_coreg(coreg_txt,[m{1} '.nii']);
  115. end
  117. % Regional voxel counts and volumes in subject space. suit_vol does not
  118. % compute the voxel volume correctly, so we use our own code.
  119. disp('Regional volumes')
  120. regional_volumes(inp.out_dir)

cersuit_main.m at commit ebcc8bb, no license · at the source

Overview

Authors: Yong Han1,2,3,4, Longyu Sheng1,3, Sanwang Wang5,6, Xin Wen7, Wenjing Lu1,3, Peng Li5,6, Zhilu Zhou1,3, Zhanrui Guo8, Yujun Gao1,9
ORCID iDs: Zhilu Zhou
  1. Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Henan Medical University,No. 207 Qianjin Road, Xinxiang, 453002 China
  2. Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Henan Medical University,Xinxiang, 453002 China
  3. Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang, 453002 China
  4. Brain Institute, Henan Academy of Innovations in Medical Science, Zhengzhou, 451163 China
  5. NHC Key Laboratory of Mental Health, Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University,Beijing, 100191 China
  6. National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital,Beijing, 100191 China
  7. Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University,Beijing, 100191 China
  8. Department of Psychiatry, Division Anxin Hospital, The Xinjiang Production and Construction Corps 13th,Hami, 839101 China
  9. Department of Psychiatry, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan University of Science and Technology,Wuhan, 430063 China
Institutions: Henan Medical University (China); Peking University Sixth Hospital (China); Peking University (China); Xinjiang Production and Construction Corps (China); Wuhan University of Science and Technology (China)
Journal: BMC psychiatry, volume 26, issue 1, article 449
Dates: received 22 December 2024; accepted 8 April 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12888-026-08071-4 · PMID 42021264 · PMCID PMC13245009 · OpenAlex W7155208924
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), depression (population), bipolar (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging, Preprocessing
Keywords: Cerebellum, Bipolar disorder, Unipolar depression, Gray matter, Structural MRI
MeSH: Bipolar Disorder*, Cerebellum*, Depressive Disorder*, Gray Matter*, Adult, Case-Control Studies, Diagnosis, Differential, Female, Humans, Machine Learning, Magnetic Resonance Imaging, Male, Middle Aged, Organ Size, Young Adult (* major topic)
Topic: Bipolar Disorder and Treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Open Project of Psychiatry and Neuroscience Discipline of Second Affiliated Hospital of Xinxiang Medical University (XYEFYJSSJ-2024-02); Henan Province science and technology research and development plan joint fund (industry) major project (235101610004); Henan Academy of Medical Sciences Fundamental Research Fund Project (JBKY250316); National College Student Innovation and Entrepreneurship Training Program (202310472050); Key Research and Development Projects of Henan Province (241111312800); Xinjiang 13th Division Xinxing City Science and Technology Plan Project (2024B14); Grassroots Health Science and Technology Innovation Project of the Health Commission of Hubei Province
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

baxpr/cersuit

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: ebcc8bbf429823c7ecca32484616f769c0f75122, 1 May 2023
Languages: MATLAB (11), Shell (7)
Size: 35 files, 18 scripts
Software Heritage: archived
Found in: the text, “Cerebellar processing”
Holds: README, environment (Dockerfile, Singularity)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: SPM (9 files), FSL (3 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
19 files
At the source: github.com/baxpr/cersuit

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1186/s12888-026-08071-4.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 15 MeSH terms, 7 funders, 62 references.

Cite

This paper

Han, Y., Sheng, L., Wang, S., Wen, X., Lu, W., Li, P., Zhou, Z., Guo, Z., & Gao, Y. (2026). Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression. BMC psychiatry, 26(1), 449. https://doi.org/10.1186/s12888-026-08071-4

BibTeX

@article{han2026cerebellar,
author = {Han, Yong and Sheng, Longyu and Wang, Sanwang and Wen, Xin and Lu, Wenjing and Li, Peng and Zhou, Zhilu and Guo, Zhanrui and Gao, Yujun},
title = {{Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression}},
journal = {BMC psychiatry},
year = {2026},
month = apr,
volume = {26},
number = {1},
pages = {449},
publisher = {BMC},
issn = {1471-244X},
doi = {10.1186/s12888-026-08071-4},
url = {https://doi.org/10.1186/s12888-026-08071-4},
pmid = {42021264},
pmcid = {PMC13245009}
}

RIS

TY - JOUR
AU - Han, Yong
AU - Sheng, Longyu
AU - Wang, Sanwang
AU - Wen, Xin
AU - Lu, Wenjing
AU - Li, Peng
AU - Zhou, Zhilu
AU - Guo, Zhanrui
AU - Gao, Yujun
TI - Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression
T2 - BMC psychiatry
J2 - BMC Psychiatry
PY - 2026
DA - 2026/04/22
VL - 26
IS - 1
SP - 449
SN - 1471-244X
PB - BMC
DO - 10.1186/s12888-026-08071-4
UR - https://doi.org/10.1186/s12888-026-08071-4
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s12888-026-08071-4",
"type": "article-journal",
"title": "Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression",
"container-title": "BMC psychiatry",
"author": [
{
"family": "Han",
"given": "Yong"
},
{
"family": "Sheng",
"given": "Longyu"
},
{
"family": "Wang",
"given": "Sanwang"
},
{
"family": "Wen",
"given": "Xin"
},
{
"family": "Lu",
"given": "Wenjing"
},
{
"family": "Li",
"given": "Peng"
},
{
"family": "Zhou",
"given": "Zhilu"
},
{
"family": "Guo",
"given": "Zhanrui"
},
{
"family": "Gao",
"given": "Yujun"
}
],
"container-title-short": "BMC Psychiatry",
"volume": "26",
"issue": "1",
"page": "449",
"DOI": "10.1186/s12888-026-08071-4",
"PMID": "42021264",
"PMCID": "PMC13245009",
"ISSN": "1471-244X",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12888-026-08071-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
22
]
]
}
}

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